Qwen 3.7 Max vs Llama 5 vs Gemini 3.5 Flash: Head-to-Head on Enterprise Multi-Agent Planning

As of May 24, 2026 (UTC) Multi-agent planning has become a critical capability for enterprise AI systems that must coordinate multiple specialized agents to solve complex operational problems. Alibaba Cloud’s Qwen 3.7 Max—the latest open-weight model in the Qwen family—has recently topped the Hugging Face trending leaderboard for multi-agent planning tasks, achieving state-of-the-art results on the Multi-Agent Planning Benchmark (MAPB). This article provides the first standalone, vendor-neutral deep dive into Qwen 3.7 Max for multi-agent planning, directly comparing it against Meta’s Llama 5 and Google DeepMind’s Gemini 3.5 Flash on three enterprise planning scenarios: supply chain contingency routing, clinical trial protocol design, and dynamic resource allocation. Why Multi-Agent Planning Is the New Frontier for Enterprise AI Enterprise operations increasingly require AI systems that c

an decompose complex workflows, delegate subtasks to specialized agents, and reason over long planning horizons. Unlike single-model chatbots, multi-agent planning systems must maintain coherence across interdependent decisions while respecting real-world constraints—budget, compliance, time. The ability to plan accurately under these conditions is a differentiator for organizations deploying AI in operations, logistics, R&D, and resource management. According to the MAPB leaderboard (artificialanalysis.ai as of May 2026), Qwen 3.7 Max achieved an overall planning accuracy of 94.3% across 120 diverse planning tasks, compared to Llama 5’s 91.7% and Gemini 3.5 Flash’s 89.2%. These results have drawn attention from enterprises evaluating models for production deployment. However, leaderboard aggregates can obscure task-specific performance. Our head-to-head evaluation drills into three real

-world scenarios. Benchmarking Methodology: Three Enterprise Planning Scenarios To ensure a fair comparison, we designed three controlled tests that mirror common enterprise planning challenges. Each test was run 20 times per model on identical prompts, with temperature set to 0.1 for reproducibility. We measured planning accuracy (percentage of generated plans that satisfy all explicit constraints), latency (time to generate a complete plan under single-agent vs. concurrent multi-agent calls), and cost (estimated API inference cost on AWS Bedrock and on-premise hardware). Supply chain contingency routing: Given a network of 15 suppliers, 8 warehouses, and 4 production sites, the model must reroute material flows after a supplier disruption, meeting lead-time and cost constraints. Clinical trial protocol design: The model must propose a phase II trial design with inclusion/exclusion crit

eria, dosing schedule, and statistical power calculation, respecting regulatory guidelines (FDA 21 CFR Part 11). Dynamic resource allocation: In a simulated cloud environment with 100 compute nodes and fluctuating demand, the model must allocate CPU, GPU, and memory to competing workloads while minimizing SLA violations. Scenario 1: Supply Chain Contingency Routing – Accuracy and Speed In this scenario, models had to generate a complete rerouting plan within 10 seconds to be operationally viable. Results: Model Planning Accuracy Avg Latency (single) Avg Latency (5 concurrent calls) :----------------- :---------------- :------------------- :------------------------------- Qwen 3.7 Max 96.2% 2.3s 4.1s Llama 5 93.8% 1.9s 3.6s Gemini 3.5 Flash 88.5% 1.2s 2.8s Qwen 3.7 Max achieved the highest accuracy, particularly in handling multi-step contingency logic and maintaining inventory compliance

. Llama 5 was slightly faster and more cost-effective for single calls but degraded more under concurrency. Gemini 3.5 Flash was fastest overall but missed subtle regulatory constraints in 11.5% of plans, requiring human revision. Takeaway: For supply chain operations where precision on constraints is paramount, Qwen 3.7 Max leads. For high-throughput, lower-stakes routing, Gemini 3.5 Flash’s speed wins. Scenario 2: Clinical Trial Protocol Design – Reasoning Depth Clinical protocol design demands multi-variable optimization: patient safety, statistical validity, and regulatory formatting. We evaluated each model on a 15-variable constraint satisfaction problem. Model Constraint Satisfaction Avg Plan Length (words) Safety Compliance :----------------- :---------------------- :---------------------- :---------------- Qwen 3.7 Max 94.1% 1,240 98.3% Llama 5 90.3% 1,180 95.1% Gemini 3.5 Flash

85.7% 1,050 89.6% Qwen 3.7 Max excelled at generating detailed protocols that adhered to FDA formatting and included proper statistical power calculations. Llama 5 was close but occasionally omitted secondary safety endpoints. Gemini 3.5 Flash’s protocols were terse and missed some required section